Affiliations: [a] Department of Computer Science and Technology, Changchun University of Science and Technology, Changchun, Jilin, China | [b] Department of Electrical and Computer Engineering, Florida International University, Miami, FL, USA
Correspondence:
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Corresponding author: Qi Li, Department of Computer Science and Technology, Changchun University of Science and Technology, Changchun 130022, Jilin, China. Tel.: +86 431 85583546; Fax: +86 431 85583562; E-mail: liqi@cust.edu.cn.
Abstract: BACKGROUND:P300-spellers are brain-computer interface (BCI)-based character input systems. Support vector machine (SVM) ensembles are trained with large-scale training sets and used as classifiers in these systems. However, the required large-scale training data necessitate a prolonged collection time for each subject, which results in data collected toward the end of the period being contaminated by the subject’s fatigue. OBJECTIVE:This study aimed to develop a method for acquiring more training data based on a collected small training set. METHODS:A new method was developed in which two corresponding training datasets in two sequences are superposed and averaged to extend the training set. The proposed method was tested offline on a P300-speller with the familiar face paradigm. RESULTS:The SVM ensemble with extended training set achieved 85% classification accuracy for the averaged results of four sequences, and 100% for 11 sequences in the P300-speller. In contrast, the conventional SVM ensemble with non-extended training set achieved only 65% accuracy for four sequences, and 92% for 11 sequences. CONCLUSION:The SVM ensemble with extended training set achieves higher classification accuracies than the conventional SVM ensemble, which verifies that the proposed method effectively improves the classification performance of BCI P300-spellers, thus enhancing their practicality.
Keywords: Brain-computer interface, familiar face paradigm, P300 speller, SVM ensemble, training set extension
